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首页> 外文期刊>The Journal of Supercritical Fluids >Reduced-order modelling of equations of state using tensor decomposition for robust, accurate and efficient property calculation in high-pressure fluid flow simulations
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Reduced-order modelling of equations of state using tensor decomposition for robust, accurate and efficient property calculation in high-pressure fluid flow simulations

机译:用张量分解对高压流体流模拟鲁棒,准确和高效的特性计算的张力分解等方程的下降阶模型

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摘要

Computationally efficient, accurate and robust Computational Fluid Dynamics (CFD) simulations involving thermodynamic properties from Equations of State (EOS) are hindered by limitations dictated by coupling strategies between EOS and CFD codes. This is a key aspect for a wide range of Chemical Engineering designs with special emphasis on those involving transcritical flows. We introduce a ROM approach based on a non-structured and sparse implementation of the Canonical Polyadic Decomposition of tensors that target abovementioned requirements. It reaches a similar speed with regards to direct use of the full equation of state and provides mean errors about 1 %-5 % without limiting accuracy. Its implementation is done in a standard and portable way, avoiding the need of additional implementation and an easy coupling with open and commercial CFD codes. The method is tested here for CFD but it can be directly applied in any process simulation tool. (C) 2020 Elsevier B.V. All rights reserved.
机译:通过通过EOS和CFD代码之间的耦合策略决定的限制来阻碍涉及从状态方程(EOS)的热力学性质的计算高效,准确和鲁棒的计算流体动力学(CFD)模拟。这是各种化学工程设计的关键方面,特别强调涉及跨临界流的人。我们基于针对目标上述要求的张量的规范多adiC分解的非结构化和稀疏实施来介绍一种ROM方法。它达到了类似的速度,以便直接使用状态的完整方程,并提供约1%-5%的平均误差而不限制精度。其实施是以标准和便携式方式完成的,避免需要额外的实现和与开放式和商业CFD代码轻松耦合。此处在此测试CFD,但它可以直接应用于任何过程仿真工具。 (c)2020 Elsevier B.V.保留所有权利。

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